Triple
T7623160
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Privy Counsellor (United Kingdom) |
E172550
|
entity |
| Predicate | oathCharacter |
P78173
|
FINISHED |
| Object | oath of secrecy |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: oath of secrecy | Statement: [Privy Counsellor (United Kingdom), oathCharacter, oath of secrecy]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: oathCharacter Context triple: [Privy Counsellor (United Kingdom), oathCharacter, oath of secrecy]
-
A.
character1
Indicates that the subject is identified as the first or primary character in a narrative or context.
-
B.
characterIn
Indicates that an entity appears as a character within a specified work, story, or narrative.
-
C.
eraCharacter
Indicates that a character is associated with, or belongs to, a particular historical or fictional era.
-
D.
characterAlignment
Indicates the moral or ethical stance a character holds, typically along axes such as good–evil and lawful–chaotic.
-
E.
narrativeCharacter
Indicates that one entity functions as a character within the narrative or story associated with another entity.
- F. None of above. chosen
Provenance (4 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69c699506b308190826894dab1d9ea86 |
completed | March 27, 2026, 2:50 p.m. |
| NER | Named-entity recognition | batch_69c6fe73ff7c8190ab1218d97b37416d |
completed | March 27, 2026, 10:02 p.m. |
| PD | Predicate disambiguation | batch_69c6f4e725a88190b1f05dd224f7f4f2 |
completed | March 27, 2026, 9:21 p.m. |
| PDg | Predicate description generation | batch_69c6fe7323b0819081664662d2f26937 |
completed | March 27, 2026, 10:02 p.m. |
Created at: March 27, 2026, 3:56 p.m.